Advanced Techniques for the Classification of Very High Resolution and Hyperspectral Remote Sensing Images

This thesis is about the classification of the last generation of very high resolution (VHR) and hyperspectral remote sensing (RS) images, which are capable to acquire images characterized by very high resolution from satellite and airborne platforms. In particular, these systems can acquire VHR multispectral images characterized by a geometric resolution in the order or smaller than one meter, and hyperspectral images, characterized by hundreds of bands associated to narrow spectral channels. This type of data allows to precisely characterizing the different materials on the ground and/or the geometrical properties of the different objects (e.g., buildings, streets, agriculture fields, etc.) in the scene under investigation. This remote sensed data provide very useful information for several applications related to the monitoring of the natural environment and of human structures. However, in order to develop real-world applications with VHR and hyperspectral data, it is necessary to define automatic techniques for an efficient and effective analysis of the data. Here, we focus our attention on RS image classification, which is at the basis of most of the applications related to environmental monitoring. Image classification is devoted to translate the features that represent the information present in the data in thematic maps of the land cover types according to the solution of a pattern recognition problem. However, the huge amount of data associated with VHR and hyperspectral RS images makes the classification problem very complex and the available techniques are still inadequate to analyze these kinds of data. For this reason, the general objective of this thesis is to develop novel techniques for the analysis and the classification of VHR and hyperspectral images, in order to improve the capability to automatically extract useful information captured from these data and to exploit it in real applications. Moreover we addressed the classification of RS images in operational conditions where the available reference labeled samples are few and/or not completely reliable (which is quite common in many real problems). In particular, the following specific issues are considered in this work: 1. development of feature selection for the classification of hyperspectral images, for identifying a subset of the original features that exhibits at the same time high capability to discriminate among the considered classes and high invariance in the spatial domain of the scene; 2. classification of RS images when the available training set is not fully reliable, i.e., some labeled samples may be associated to the wrong …